Parameter Estimation based on Neoteric Ranked Set Samples with Applications to Weibull Distribution. | ||||
| The Egyptian Statistical Journal | ||||
| Article 1, Volume 68, Issue 2, December 2024, Page 15-23 PDF (578.24 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/esju.2024.291355.1035 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mohamed A. Sabry; Essam A. Amin; Abdeltawab A. Gira 
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| Department of Mathematical Statistics, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt | ||||
| Abstract | ||||
| This paper investigates the parameter estimation in a Weibull distribution using Neoteric Ranked Set Sampling (NRSS) designs, thus extending the earlier study by Sabry et al. (2020). Maximum likelihood estimators (MLEs) using NRSS, comparing their performance against estimators from Simple Random Sampling (SRS) and traditional Ranked Set Sampling (RSS) were derived and evaluated. A Monte Carlo simulation was used to evaluate their performances with respect to metrics that include absolute relative bias, mean squared error, and overall efficiency. The results showed that the NRSS estimators performed better than SRS and RSS estimators for a Weibull distribution It demonstrates that NRSS-based estimators consistently outperform their SRS and RSS counterparts, exhibiting lower bias and higher efficiency. This is important because in many real-world situations, collecting data can be expensive or time-consuming. The study's findings strongly suggest that NRSS designs offer a compelling alternative for achieving more accurate parameter estimates in relevant applications. | ||||
| Keywords | ||||
| Simple Random Sampling; Ranked Set Sampling; Neoteric Ranked Set Sampling; Estimation Parameter; Maximum Likelihood Estimation | ||||
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